Results: (1) EQ-5D was valid and responsive for skin conditions and most cancers; in vision, its
performance varied according to aetiology; and performance was poor for hearing impairments. The HUI3
performed well for hearing and vision disorders. It also performed well in cancers although evidence was
limited and there was no evidence in skin conditions. There were limited data for SF-6D in all four
conditions and limited evidence on reliability of all instruments. (2) Mapping algorithms were estimated to
predict EQ-5D values from alternative cancer-specific measures of health. Response mapping using all the
domain scores was the best performing model for the EORTC QLQ-C30. In an exploratory analysis, a
limited dependent variable mixture model performed better than an equivalent linear model. In the full
analysis for the FACT-G, linear regression using ordinary least squares gave the best predictions followed
by the tobit model. (3) The exploratory valuation study found that bolt-on items for vision, hearing and
tiredness had a significant impact on values of the health states, but the direction and magnitude of
differences depended on the severity of the health state. The vision bolt-on item had a statistically
significant impact on EQ-5D health state values and a full valuation model was estimated.
Conclusions: EQ-5D performs well in studies of cancer and skin conditions. Mapping techniques
provide a solution to predict EQ-5D values where EQ-5D has not been administered. For conditions
where EQ-5D was found to be inappropriate, including some vision disorders and for hearing, bolt-ons
provide a promising solution. More primary research into the psychometric properties of the generic
preference-based measures is required, particularly in cancer and for the assessment of reliability.
Further research is needed for the development and valuation of bolt-ons to EQ-5D.
Funding: This project was funded by the UK Medical Research Council (MRC) as part of the MRC-NIHR
methodology research programme (reference G0901486) and will be published in full in Health Technology
Assessment; Vol. 18, No. 9. See the NIHR Journals Library website for further project information.